The subject matter disclosed herein relates generally to control systems, and in particular to a control system for a reconfigurable rotary wing aircraft.
Vehicles, such as rotary wing aircraft, typically employ a control system that receives operator commands and interfaces those commands to components of the aircraft. For example, existing rotary wing aircraft may employ a primary flight control system (PFCS) and an automatic flight control system (AFCS) that receive operator commands and control aircraft operation. These control systems also sense vehicle status to provide feedback and improve control of the rotary wing aircraft.
A given control system may have a plurality of goals and a plurality of limits. Limits are inequality constraints on system dynamic variables. An example limit may be to prevent an engine temperature from exceeding a certain temperature in order to prevent meltdown or rapid deterioration. Another might be to prevent a rotor speed from exceeding a certain angular velocity to maintain structural integrity and aircraft control power. An example goal may be to achieve a certain engine thrust level, such as a thrust of 10,000 pounds. While it is desirable to achieve goals, it is necessary to meet limits. A multivariable system may include a number of effectors that can be adjusted to meet system goals and limits. In some cases, a system may be cross-coupled, which means that each effector change may affect goals and limits with varying dynamics. In a cross-coupled system, it is not possible to change a single effector in isolation to affect only a single goal or limit, as a change in one effector may affect a plurality of goals or limits. Calculating effector commands in a cross-coupled multi-variable system can therefore be complex and computationally demanding.
Model predictive control (MPC) is a multivariable control theory that explicitly includes limits and, therefore, provides a good match with practical systems. MPC can also be configured to respond in realtime to changes in the system, such as actuator faults. Thus, MPC provides a formal method of control algorithm design for multivariable systems that can decouple responses, as well as physically possible, even as limits are hit and faults occur.